Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics

Gouveia, Liana Ribeiro; Santos, Joelma Carvalho; Silva, Ronaldo Dionísio; Batista, Andrea Dória; Domingues, Ana Lúcia Coutinho; Lopes, Edmundo Pessoa; Silva, Ricardo Oliveira

doi:10.1371/journal.pone.0182196

Cited by 16 publications

(11 citation statements)

References 32 publications

(38 reference statements)

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“…At present, schistosomiasis is diagnosed by a clinical history of contact with fresh water from endemic foci, followed by both direct methods and indirect tests in the clinical laboratory. The former includes using the Kato–Katz technique to examine the feces under light microscopy for detection of eggs, while the latter includes using immunological approaches, such as detecting soluble antigens secreted from eggs via the antigen–antibody reaction ( 2 , 6 , 7 ). Imaging methods, for instance, ultrasonography (US), CT scan and MR, scan, are established to inspect the presence of periportal fibrosis ( 7 ).…”

Section: Introductionmentioning

confidence: 99%

“…The former includes using the Kato–Katz technique to examine the feces under light microscopy for detection of eggs, while the latter includes using immunological approaches, such as detecting soluble antigens secreted from eggs via the antigen–antibody reaction ( 2 , 6 , 7 ). Imaging methods, for instance, ultrasonography (US), CT scan and MR, scan, are established to inspect the presence of periportal fibrosis ( 7 ). Nevertheless, these diagnostic methods are not sensitive or specific enough and are not suitable for early diagnosis; therefore, a well-suited approach with high sensitivity and specificity is urgently required to detect acute stage infection.…”

Section: Introductionmentioning

confidence: 99%

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Alterations of Gut Microbiome and Metabolite Profiling in Mice Infected by Schistosoma japonicum

Chen

et al. 2020

Front. Immunol.

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Schistosoma japonicum ( S. japonicum ) is one of the etiological agents of schistosomiasis, a widespread zoonotic parasitic disease. However, the mechanism of the balanced co-existence between the host immune system and S. japonicum as well as their complex interaction remains unclear. In this study, 16S rRNA gene sequencing, combined with metagenomic sequencing approach as well as ultraperformance liquid chromatography–mass spectrometry metabolic profiling, was applied to demonstrate changes in the gut microbiome community structure during schistosomiasis progression, the functional interactions between the gut bacteria and S. japonicum infection in BALB/c mice, and the dynamic metabolite changes of the host. The results showed that both gut microbiome and the metabolites were significantly altered at different time points after the infection. Decrease in richness and diversity as well as differed composition of the gut microbiota was observed in the infected status when compared with the uninfected status. At the phylum level, the gut microbial communities in all samples were dominated by Firmicutes, Bacteroidetes, Proteobacteria, and Deferribacteres, while at the genus level, Lactobacillus, Lachnospiraceae NK4A136 group, Bacteroides, Staphylococcus , and Alloprevotella were the most abundant. After exposure, Roseburia , and Ruminococcaceae UCG-014 decreased, while Staphylococcus, Alistipes , and Parabacteroides increased, which could raise the risk of infections. Furthermore, LEfSe demonstrated several bacterial taxa that could discriminate between each time point of S. japonicum infection. Besides that, metagenomic analysis illuminated that the AMP-activated protein kinase (AMPK) signaling pathway and the chemokine signaling pathway were significantly perturbed after the infection. Phosphatidylcholine and colfosceril palmitate in serum as well as xanthurenic acid, naphthalenesulfonic acid, and pimelylcarnitine in urine might be metabolic biomarkers due to their promising diagnostic potential at the early stage of the infection. Alterations of glycerophospholipid and purine metabolism were also discovered in the infection. The present study might provide further understanding of the mechanisms during schistosome infection in aspects of gut microbiome and metabolites, and facilitate the discovery of new targets for early diagnosis and prognostic purposes. Further validations of potential biomarkers in human populations are necessary, and the exploration of interactions among S. japonicum , gut microbiome, and metabolites is to be deepened in the future.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Alterations of Gut Microbiome and Metabolite Profiling in Mice Infected by Schistosoma japonicum

Chen

et al. 2020

Front. Immunol.

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“…Qiao et al[18] studied schizophrenia in patients treated with olanzapine through the metabolic analysis of blood plasma. Gouveia et al[19] developed metabonomics models to investigate periportal liver fibrosis caused by mansonic schistosomiasis among patients diagnosed with viral hepatitis. Batista et al [20] discriminated intermediate from advanced liver fibrosis in patients using 1 H NMR-based metabonomics.…”

Section: Introductionmentioning

confidence: 99%

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics

et al. 2019

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This is a report on how 1 H NMR-based metabonomics was employed to discriminate osteopenia from osteoporosis in postmenopausal women, identifying the main metabolites associated to the separation between the groups. The Assays were performed using seventy-eight samples, being twenty-eight healthy volunteers, twenty-six osteopenia patients and twenty-four osteoporosis patients. PCA, LDA, PLS-DA and OPLS-DA formalisms were used. PCA discriminated the samples from healthy volunteers from diseased patient samples. Osteopenia-osteoporosis discrimination was only obtained using Analysis Discriminants formalisms, as LDA, PLS-DA and OPLS-DA. The metabonomics model using LDA formalism presented 88.0% accuracy, 88.5% specificity and 88.0% sensitivity. Cross-Validation, however, presented some problems as the accuracy of modeling decreased. LOOCV resulted in 78.0% accuracy. The OPLS-DA based model was better: R2Y and Q2 values equal to 0.871 (p<0.001) and 0.415 (p<0.001). LDA and OPLS-DA indicated the important spectral regions for discrimination, making possible to assign the metabolites involved in the skeletal system homeostasis, as follows: VLDL, LDL, leucine, isoleucine, allantoin, taurine and unsaturated lipids. These results indicate that 1 H NMR-based metabonomics can be used as a diagnosis tool to discriminate osteoporosis from osteopenia using a single serum sample.

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“…The method has been studied in various liver diseases[ 13 ], and has been shown to be useful in distinguishing between patients with viral hepatitis and healthy volunteers[ 14 - 15 ] and performing well at identifying complications of liver cirrhosis[ 16 - 18 ]. Recently, our group showed that a partial least squares discriminant analysis (PLS-DA) metabonomic model (MM), based on the H-1 NMR spectroscopy of serum samples, presented a clear separation between 18 patients coinfected with schistosomiasis mansoni and hepatitis B virus (HBV) or HCV and 22 HBV or HCV mono-infected patients, with an accuracy, a predictive ability (Q 2 ) and a coefficient of determination (R 2 ) of 100%, 98.1% and 97.5%, respectively[ 19 ]. Therefore, the aim of this study was to develop and evaluate MMs, using 1 H NMR spectrum of serum samples, as non-invasive markers of significant liver fibrosis, advanced liver fibrosis and cirrhosis in patients with chronic hepatitis C (CHC), and to compare their performance with the APRI and FIB-4.…”

Section: Introductionmentioning

confidence: 99%

Proton nuclear magnetic resonance-based metabonomic models for non-invasive diagnosis of liver fibrosis in chronic hepatitis C: Optimizing the classification of intermediate fibrosis

Batista¹,

Barros²,

Costa³

et al. 2018

WJH

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AIMTo develop metabonomic models (MMs), using 1H nuclear magnetic resonance (NMR) spectra of serum, to predict significant liver fibrosis (SF: Metavir ≥ F2), advanced liver fibrosis (AF: METAVIR ≥ F3) and cirrhosis (C: METAVIR = F4 or clinical cirrhosis) in chronic hepatitis C (CHC) patients. Additionally, to compare the accuracy of the MMs with the aspartate aminotransferase to platelet ratio index (APRI) and fibrosis index based on four factors (FIB-4).METHODSSixty-nine patients who had undergone biopsy in the previous 12 mo or had clinical cirrhosis were included. The presence of any other liver disease was a criterion for exclusion. The MMs, constructed using partial least squares discriminant analysis and linear discriminant analysis formalisms, were tested by cross-validation, considering SF, AF and C.RESULTSResults showed that forty-two patients (61%) presented SF, 28 (40%) AF and 18 (26%) C. The MMs showed sensitivity and specificity of 97.6% and 92.6% to predict SF; 96.4% and 95.1% to predict AF; and 100% and 98.0% to predict C. Besides that, the MMs correctly classified all 27 (39.7%) and 25 (38.8%) patients with intermediate values of APRI and FIB-4, respectively.CONCLUSIONThe metabonomic strategy performed excellently in predicting significant and advanced liver fibrosis in CHC patients, including those in the gray zone of APRI and FIB-4, which may contribute to reducing the need for these patients to undergo liver biopsy.

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Diagnosis of coinfection by schistosomiasis and viral hepatitis B or C using 1H NMR-based metabonomics

Cited by 16 publications

References 32 publications

Alterations of Gut Microbiome and Metabolite Profiling in Mice Infected by Schistosoma japonicum

Alterations of Gut Microbiome and Metabolite Profiling in Mice Infected by Schistosoma japonicum

Osteopenia-osteoporosis discrimination in postmenopausal women by 1H NMR-based metabonomics

Proton nuclear magnetic resonance-based metabonomic models for non-invasive diagnosis of liver fibrosis in chronic hepatitis C: Optimizing the classification of intermediate fibrosis

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